2023/09/09 by Shusuke Kasamatsu, Yuichi Motoyama, Kasamatsu, Shusuke +5 · 2 citations
Chemical Engineering · Materials Science · #Electronic and Structural Properties of Oxides #FOS: Physical sciences #Ionic liquids properties and applications #Machine Learning in Materials Science #Materials Science (cond-mat.mtrl-sci) #Statistical Mechanics (cond-mat.stat-mech)
paper · pdf · doi:10.48550/arxiv.2309.04769
openalex publication_date 2023/09/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Simulation of the intermediate levels of disorder found in multi-component multi-sublattice systems in various functional materials is a challenging issue, even for state-of-the-art methodologies based on first-principles calculation. Here, we introduce our open-source package ab Initio Configuration Sampling Toolkit (abICS), which combines high-throughput first-principles calculations, machine learning, and parallel extended ensemble sampling in an active learning setting to enable such simulations. The theoretical background is reviewed in some detail followed by brief notes on usage of the software. In addition, our recent applications of abICS to multi-component ionic systems and their interfaces for energy applications are reviewed as demonstration of the power of this approach.